Intelligent Transportation Planning Research Articles

Data-driven bottleneck detection on Tehran highways

In metropolitan areas, traffic congestion has become a prevalent challenge due to rapid urbanization and increased vehicle usage, adversely impacting mobility, productivity, and quality of life. Identifying and mitigating persistent traffic bottlenecks is crucial for developing efficient transportation systems and guiding infrastructure planning decisions. This research proposes an innovative data-driven methodology to pinpoint recurrent traffic bottlenecks in Tehran's extensive highway network, addressing the limitations of traditional traffic monitoring methods. Through data mining and image processing techniques applied to 16 months of traffic flow maps from Google Maps, diverse information is extracted, including traffic nodes, congestion hotspots, and locations with the longest queue lengths. The image processing approach involves color-based segmentation, pixel-level analysis, and machine learning algorithms to determine congestion levels across the highway network. The identified bottlenecks are validated against ground truth data from CCTV cameras, demonstrating a remarkable 92 % correlation for key identified points. The proposed approach leverages the power of advanced analytics to comprehensively analyze all major highways, including areas lacking CCTV infrastructure. The robust validation process reinforces the reliability of this data-driven solution in capturing real-world traffic dynamics. As urban mobility challenges escalate globally, the integration of diverse data sources and cutting-edge techniques will be instrumental in guiding intelligent transportation planning and policy decisions.

Intelligent Transportation Planning and Construction in the Era of Big Data

Intelligent transportation planning has become increasingly important in the era of big data. By utilizing big data technology, traffic flow can be measured and managed more effectively. This leads to transportation planning that is more scientific and provides people with more convenient travel options. In this paper, the role of big data technology in intelligent transportation planning is analyzed. Subsequently. a path towards intelligent transportation planning is proposed,laying the foundation for intelligent transportation development.

Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data.

With growing environmental concerns and the exploitation of ubiquitous big data, smart transportation is transforming logistics business and operations into a more sustainable approach. To answer questions in intelligent transportation planning, such as which data are feasible, which methods are applicable for intelligent prediction of such data, and what are the available operations for prediction, this paper offers a new deep learning approach called bi-directional isometric-gated recurrent unit (BDIGRU). It is merged to the deep learning framework of neural networks for predictive analysis of travel time and business adoption for route planning. The proposed new method directly learns high-level features from big traffic data and reconstructs them by its own attention mechanism drawn by temporal orders to complete the learning process recursively in an end-to-end manner. After deriving the computational algorithm with stochastic gradient descent, we use the proposed method to perform predictive analysis of stochastic travel time under various traffic conditions (especially for congestions) and then determine the optimal vehicle route with the shortest travel time under future uncertainty. Based on empirical results with big traffic data, we show that the proposed BDIGRU method can (1) significantly improve the predictive accuracy of one-step 30 min ahead travel time compared to several conventional (data-driven, model-driven, hybrid, and heuristics) methods measured with several performance criteria, and (2) efficiently determine the optimal vehicle route in relation to the predictive variability under uncertainty.

T-PORP: A Trusted Parallel Route Planning Model on Dynamic Road Networks

Route planning over dynamic road networks is an increasingly fundamental problem of modern transportation systems for human society, especially in the field of Intelligent Supply Chain (ISC). Due to the high degree of urbanization and the high number of vehicles, longer response time caused by massive concurrent queries, as well as more attacks caused by malicious vehicles, results in low efficiency of the transportation system and huge waste of computation resources. Thus, it is necessary to provide an efficient and safe transportation service for intelligent transportation planning. To achieve it, we utilize and improve the trust model to prevent the waste of computation resources. Meanwhile, we introduce a Trusted Parallel Optimization on Route Planning (T-PORP) based on Dual-level Grid (DLG) index to continuously handle the process of route planning in parallel. Considering the evolving traffic condition, we employ an LSTM (Long Short-Term Memory) neural network to periodically predict the weights of roads. Experimental results indicate that T-PORP is effective to sorts of trust model attacks and reduces the response time by an average of about 46.7% and saves the processing time by an average of about 27.6% compared with CANDS (Continuous Optimal Navigation via Distributed Stream Processing) algorithm.

Machine and Deep Learning Technologies, Location Tracking and Obstacle Avoidance Algorithms, and Cognitive Wireless Sensor Networks in Intelligent Transportation Planning and Engineering

Machine and Deep Learning Technologies, Location Tracking and Obstacle Avoidance Algorithms, and Cognitive Wireless Sensor Networks in Intelligent Transportation Planning and Engineering

Predictive Control Algorithms, Real-World Connected Vehicle Data, and Smart Mobility Technologies in Intelligent Transportation Planning and Engineering

Predictive Control Algorithms, Real-World Connected Vehicle Data, and Smart Mobility Technologies in Intelligent Transportation Planning and Engineering

Research on Optimal Vehicle Path in Intelligent Transportation

The rapid development of economy and science and technology is quite obvious, which makes cars becoming more and more widely into the general family life. With the rapid development of economics and technology; the number of vehicles has largely increased. In this paper, there is the basic organizational framework for intelligent transportation, intelligent transportation network proposed model and its data storage structure, and the important influence on optimal path trajectory intelligent transportation planning. This paper analyzes intersection road network in the distribution based on computer graphics language, type and grade roads. The dynamic mathematical model of single vehicle is used vehicle planning algorithm based on period, effectively avoided the traffic road, reduce vehicle travel cost, improve the real-time effect and accuracy of the vehicle dynamic path. We hope the results and researches could combine with reality in order to reduce traffic congestion.

Opportunistic planning for a fleet of transportation robots

The Dynamic Transportation-Planning Problem (DTPP) embodies a class of real-world applications that involve the reactive routing and scheduling of a fleet of vehicles in response to dynamically changing transportation demands. Examples include mobile robots in a warehouse, taxis in an urban road network, or aeroplanes for medical evacuation. In contrast with the Vehicle Routing Problem, for which a plethora of techniques is available, few approaches exist that permit the efficient deployment of a large number of vehicles in a changing environment. This paper highlights the characteristic features of the problem, reviews possible approaches and existing techniques, and proposes a heuristic solution to the DTPP using a Blackboard-based approach. The resulting application is an intelligent transportation planning system (ITPS) for a fleet of automated robot taxis, based on the generic assumption-based truth maintained Blackboard shell (GATMBS) and comprising traffic simulation models as well as various monitoring and problem-solving strategies for assignment and routing. The Blackboard architecture supports the dynamic alteration of planned routes in response to changes in traffic conditions and passenger requests. A prototype of the ITPS has been validated in simulation using a small fleet of robot taxis with randomly generated road networks, background traffic load, and passenger requests.